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Diabetes mellitus is a common illness associated with high morbidity and mortality rates. Early detection of diabetes is essential to prevent long-term health complications. The existing machine learning model struggles with accuracy and reliability issues, as well as data imbalance, hindering the creation of a dependable diabetes prediction model. The research addresses the issue using a novel deep learning mechanism called convolutional gated recurrent unit (CGRU), which could accurately detect diabetic disorder and their severity level. To overcome these obstacles, this study presents a brand-new deep learning technique, the CGRU, which enhances prediction accuracy by extracting temporal and spatial characteristics from the data. The proposed mechanism extracts both the spatial and temporal attributes from the input data to enable efficient classification. The proposed framework consists of three primary phases: data preparation, model training, and evaluation. Specifically, the proposed technique is applied to the BRFSS dataset for diabetes prediction. The collected data undergoes pre-processing steps, including missing data imputation, irrelevant feature removal, and normalization, to make it suitable for further processing. Furthermore, the pre-processed data is fed to the CGRU model, which is trained to identify intricate patterns indicating the stages of diabetes. To group the patients based on their characteristics and identity patterns, the research uses the clustering algorithm which helps them to classify the severity level. The efficacy of the proposed CGRU framework is demonstrated by validating the experimental findings against existing state-of-the-art approaches. When compared to existing approaches, such as Attention-based CNN and Ensemble ML model, the proposed model outperforms conventional machine learning techniques, demonstrating the efficacy of the CGRU architecture for diabetes prediction with a high accuracy rate o f 99.9%. Clustering algorithms are more beneficial as they help in identifying the subtle pattern in the dataset. When compared to other methods, it can lead to more accurate and reliable prediction. The study highlights how the cutting-edge CGRU model enhances the early detection and diagnosis of diabetes, which will eventually lead to improved healthcare outcomes. However, the study limits to work on diverse datasets, which is the only thing considered to be the drawback of this research.

Diabetes mellitus has become a global epidemic, with an increasing number of individuals affected by this chronic metabolic disorder. Effective management of diabetes requires a comprehensive self-care approach, which encompasses various aspects like monitoring blood glucose levels, adherence to medication, modifications in lifestyle, and regular healthcare monitoring. Innovative techniques for bettering diabetic self-care management have been developed recently as a result of developments in technology and healthcare systems. This comprehensive review examines the modern methods that have emerged to enhance diabetes self-care management systems. The review focuses on the integration of technology, Behavioural Change Techniques (BCTs), behavioural health theories such as Transtheoretical Model (TTM), the Health Belief Model (HBM), Theory of Reasoned Action/Planned Behaviour (TPB), Social Cognitive Theory (SCT) techniques to promote optimal diabetes care outcomes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 standards were followed in this research's documentation. The Systematic Literature Review (SLR) period, which covered 2009 to 2020, was used to acquire the most recent complete review. Overall, the SLR results show that self-care interventions have a favourable impact on behaviours modification, the encouragement of good lifestyle habits, the lowering of blood glucose scales, and the accomplishment of significant weight loss. According to the review's findings, treatments for diabetic self-management that included behavioural health theories and BCTs in their creation tended to be more successful. In order to assist academics and practitioners with the creation of future applications, the restriction and future direction were finally defined. After recognising the potential for combining BCT methodologies and theories, it creates self-management interventions. Depending on these recognised cutting-edge mechanisms, the current SLR can assist application developers create a model to construct efficient self-care interventions for diabetes.

Hypertension is currently rising steadily among the world population. The first level of screening to know whether one is suffering from hypertension is essential as this lays the foundation for the actual diagnosis. This research details the user interface design and usability evaluation of the hypertension management guideline. The proposed mobile application prototype assists people in screening themselves with regards to hypertension based on symptoms. This prototype also acts as a sharing platform for hypertension patients to help them share their concerns and advice within the related online community. The eye-tracker experiment was used to support the visual strategy of the prototype design. In studying the usability of the mobile application, an experiment carried out with two groups of people, of which one group of people have hypertension. In contrast, the other group of people do not have hypertension. An independent-samples t-test conducted to compare the user performance scores using the proposed prototype. Based on the usability study, both user groups understood and used the applications with ease. However, the findings revealed there was a significant difference in overall scores for hypertension patients and non-hypertension patients. The findings of this study could help software developer design an effective application for hypertension guideline tool for monitoring health and well-being.

In communication and computer networks, the Device Placement Location (DPL) is concerned with locating the placement of the corresponding electronic device (such as Hub, switches, routers... etc.) within certain existing user locations. It considers the cost of the device placement location and routing users. This will involve deciding which user locations will have devices placed at them as well as deciding an assignment of users to device locations. This paper measures the reliability of the DPL and assigns the users to the DPL by building the DPLRC & DPLRC algorithms. In fact, our model is similar in spirit to the DPL COST. However, there is a striking difference between them. Here, the users are assigned to the device locations with maximum of network reliability. It is implemented to run in O (nm log (n2/m)) time. In this paper, we discuss the relationship between device locations, routing users, and the effectiveness of the cost on the increase in the DPLs with the aid of tradeoff curves and tables. We also look at the performance of the algorithms by measuring the CPU time taken to find the DPLs.

Human's cherished and respectable desires could be fulfilled by social integration through interaction with their friends and families. These kinds of interactions are critical for the elderly, particularly for someone who has retired. Online social communities could assist them and offer a beneficial impact on the elderly. However, because the elderly people are hesitant to use new technology, researchers have attempted to integrate specially built social networking applications into simple user-interface gadgets for the elderly through the context aware systems. A proper understanding amongst the aged and the supporting community people is needed for optimal execution of the platform. The study presents a 4W framework (Who, What, Where, When) to effectively comprehend and portray the online social interaction community model's application in assisting the elderly in satisfying their unmet needs, as well as to improve the system's efficiency in addressing the elderly's unfulfilled demands. It is essential to discover what the users are keen on and provide a chance for the community group to take good decisions by utilizing the insights gained from these events.

Social integration through communication with family and friends can fulfill human’s desires of being cherished and respected. Such communications are very important for the elderly people, especially for those who have retired. Online social communities can help with this and provide positive effect on elderly people. But the elderly are quite reluctant to work with new technologies and hence, researchers have tried to implement specially designed social media application in easy user-interface devices for the elderly. In this paper, we conduct a Systematic Literature Review (SLR) method to collect and review studies to understand the different user-interaction devices used for the elderly to promote social connection. 33 literature papers were identified within the years 2013–2019 following a review procedure, which presents research on online social communities for elders. The papers are analyzed and classified further to understand the current state-of-the-art focus. This study further offers related discussion and conclusions.

Pregnancy monitoring plays a pivotal role in ensuring the well-being of both the mother and the fetus. Accurate and timely classification of fetal health is essential for early intervention and appropriate medical care. This work presents a novel method for classifying fetal health optimally by combining the Bat Algorithm (BA) in an effective manner with a hybrid model that combines Recurrent Neural Networks (RNN) and Extreme Gradient Boosting (XGB). The Bat Algorithm, inspired by the echolocation behaviour of bats, is employed to optimize the hyperparameters of the XGB-RNN hybrid model. This enables the model to adapt dynamically to the complexities of fetal health data, enhancing its performance and predictive accuracy. The XGB-RNN hybrid model is designed to capitalize on the strengths of both algorithms. XGB provides superior feature selection and gradient boosting capabilities, while RNN excels in capturing temporal dependencies in the data. This approach effectively deals with the difficulties involved in classifying fetal health in the context of pregnancy monitoring by combining these approaches. Python is used to implement the proposed framework. To validate the performance of the proposed approach, extensive experiments were conducted on a comprehensive dataset comprising a wide range of physiological parameters related to fetal health. When it comes to fetal health, BAT Algorithm's XGB-RNN (BARXG) performs outstandingly, greater than other classifiers in terms of accuracy, sensitivity, and specificity. The proposed BARXG model has greater accuracy (98.2%) than existing techniques, which include SVM, Random Forest Classifier, LGBM, Voting Classifier, and EHG.